Ship AIS Trajectory Prediction Algorithm Based on Federated Learning
Federated learning,a distributed machine learning method,effectively addresses the data island problem in environments with weak communication.This study introduces an algorithm for predicting ship trajectories,employing the Fedves federated learning framework and a Convolutional Neural Network-Gated Recurrent Unit(CNN-GRU)model,called E-FVTP.The Fedves framework standardizes dataset sizes and client regularization terms,mitigating the influence of non-independent and identically distributed features on the global model.This approach preserves original client data features,thereby accelerating the convergence speed.In maritime scenarios with limited communication resources,the CNN-GRU model utilizes Automatic Identification System(AIS)data to overcome the computational limitations of vessel terminals.Experimental evaluations on the open-source MarineCadastre and Zhoushan marine ship navigation AIS datasets demonstrate that E-FVTP reduces prediction error by 40%compared to centralized training methods.It also achieves a 67%faster convergence rate and reduces communication costs by 76.32%.These advancements enable accurate vessel trajectory predictions in complex maritime settings,significantly ensuring maritime traffic safety.